Research Article
Research on Fractal Image Coding Method Based on SNAM Segmentation Scheme
@INPROCEEDINGS{10.1007/978-3-031-04409-0_25, author={Jie He and Hui Guo and Caixu Xu and Jingjing Li}, title={Research on Fractal Image Coding Method Based on SNAM Segmentation Scheme}, proceedings={Machine Learning and Intelligent Communications. 6th EAI International Conference, MLICOM 2021, Virtual Event, November 2021, Proceedings}, proceedings_a={MLICOM}, year={2022}, month={5}, keywords={Fractal image coding SNAM segmentation Threshold optimization Human visual system}, doi={10.1007/978-3-031-04409-0_25} }- Jie He
Hui Guo
Caixu Xu
Jingjing Li
Year: 2022
Research on Fractal Image Coding Method Based on SNAM Segmentation Scheme
MLICOM
Springer
DOI: 10.1007/978-3-031-04409-0_25
Abstract
Adaptability of the partition method of fractal image compression to gray level textures directly influences the total number of partition blocks and image decoding effects. Hence, it is of critical significance to find a partition method which can accurately reflect image gray level distribution and visual threshold linkage relations in order to speed up encoding and enhance de-coding quality. Therefore, in this paper, the SNAM (Square of Non-symmetry and Anti-packing Model) partition method is optimized by thresholds. The optimized method is employed to improve fractal encoding. On the basis of the organic relations between local image textures, human vision threads and encoding efficiency as well as decoding quality, a self-adaption sub-blocks partition method based on a square non-symmetry, anti-packing model and human vision system is proposed. With such method, partitioned image sub-blocks can accurately reflect gray level distribution of images, while the number of partitioned image sub-blocks is reduced. In this way, the calculation and matching times are reduced in encoding. Encoding time is reduced in addition to improvement of restored image quality. Compared with the basic fractal encoding method, the speed is increased by over 30 times.
